Basic Plot Types

The basic structure of a gpplot looks as follows. You call ggplot, pass over some data, specify aesthetics using aes() and detmine the geometry using one (or several) of the following (or many more) options:

• geom_point()
• geom_bar()
• geom_boxplot()
• geom_col()
• geom_hist()
• geom_density()

Let’s create our first plot of the data. We are plotting a barplot of the data’s column familiar_reproducibility which (non surprisingly) shows us how familiary respondents were with the term ‘Open Science’.

ggplot(baker2016, aes(x= flagged)) + 
  geom_bar()

Now imagine we wanted to use that same plot structure to plot some other variable, i.e the respondents research field. To do so, we’d just replace x=familiar_reproducibility with x=field. Easy. Remember, you can check what other columns are available by running colnames(baker2016) on the command line. Try out a few.

Let’s try out a different plot type. Histograms can be a great way to plot data distributions. Let’s plot participants’ responses to the question ‘What percentage of the research in your field do you think is reproducible?’. The column you are looking for is called proportion_reproducible, so we would plot the histogram as follows:

ggplot(baker2016, aes(x = proportion_reproducible)) + 
  geom_histogram(stat="count")

What is your message?

The participants in the study came from different fields. We may be interested in seeing how the responses differed between the fields. GGplot allows you to use color and grouping options in plots. I.e. we may create a stacked histogramm by telling ggplot to fill the bars by field, specifying fill=field. In other plots you may choose the option color=field - color usually refers to the outlines of plotted items, whereas fill well… “fills” them. Feel free to five this a try on the histogramm to see the difference.

ggplot(baker2016, aes(group= field, fill=field, x = proportion_reproducible)) + 
  geom_histogram(stat='count')

Remember we wanted to get an idea of how responses differed between the fields. What are we learning from the previous plot? Probably, not much. So while a histogramm may have helped us understand that most participants appear to have a great amount of trust in the results in their fields, a stacked histogramm might not be the plot that allows us to understand how results differ between fields.

A good way to disect differences between groups in your data is to use facet_wrap to create different panels for each group. Simply add facet_wrap(facets = baker2016$field) to get one histogram per group. Hopefully, this helps us see how the distributions differ between the fields.

ggplot(baker2016, aes(group= field, fill=field, x = proportion_reproducible)) + 
  geom_histogram(stat = 'density') + 
  facet_wrap(facets = baker2016$field)

ggplot(baker2016, aes(x=dummy_age, y=dummy_dat2)) + 
  geom_point()

ggplot(baker2016, aes(x=dummy_age, y=dummy_dat2, color=as.factor(dummy_group))) + 
  geom_point()+
  geom_smooth(method = 'lm')

ggplot(baker2016, aes(x=dummy_age, y=dummy_dat2, color=as.factor(dummy_group))) + 
  geom_point()+
  geom_smooth(method = 'lm')

Statistics in figures - or how not to fool your audience (and yourself)

Choosing how to plot your data is crucial to communicate your message. As a first step though you may often use plots to get an idea of what your data looks like. In both cases, make sure to choose a representation that enables you to understand your data well.

Here, I am creating an artificial dataset to illustrate some points on how (not to) plot your data.

response1 = rnorm(n = 1000, mean = 10, sd = c(1,2))
response2 = rnorm(n = 1000, mean = c(2,3), sd = 1)
response3 = rnorm(n = 1000, mean = c(4,5), sd = c(1,1.5))
group = rep(letters[1:2], length.out = 100)
df = data.frame(group = as.factor(group), response1=response1, response2=response2, response3=response3)

Let’s have a look at response1. You may think it’s a good idea to first plot this data as a barplot, displaying the mean value for response1 in groups a and b. This is what such a plot would look like:

ggplot(df, aes(x=group,y=response1, color=group, fill=group, alpha=0.2)) + 
  geom_bar(stat='summary', fun=mean) 

Now that’s not very helpful. It looks as if both groups are the same.

Now you may have noticed that when I created the data, I intentionally created both groups to have the same mean value. However, their standard deviations are very different!

The following plots display the exact same data - but now in a more useful format.

par(mfrow=c(2,1))
ggplot(df, aes(x=group,y=response1, color=group, fill=group, alpha=0.2)) + 
  geom_violin()

ggplot(df, aes(x=group,y=response1, color=group, fill=group, alpha=0.2)) + 
  geom_boxplot()

Consider having a play around with response2 and response3 to see what kind of plots hide or highlight the differences between those variables.

Wide and Long Format

The data as it is right now is in the so-called wide format. For many types of plots that maynot be useful.

Data in the wide format displays repeated responses in a single row, and each response is in a separate column. Let’s have a look at the question: ‘Have you ever tried to replicate a result, either your own or someone elses, and failed?’. Subjects can say yes or no to both the idea that they have tried to replicate someone else’s results and failed, coded in try_failOwn, and that they have tried to replicate their own work and failed, coded in try_failElse. In the wide format each subject is a row, and each responses is coded as their own column:

An alternative representation would be to have a column indicating which part of the question subjects repsonded to, i.e. own or someone else’s, with each subject being represented twice: One row encodeing their response to the first, the second row encoding their response to the second question.

tmp = gather(baker2016[,c(1,20,21,27)], question, response, 2:3)
knitr::kable(
  head(tmp[order(tmp$responseid),]), # This is the table we will plot
  booktabs = TRUE)

In order to get our data into this format, we make use of the gather function from the tidyr package. This functions takes in the arguments

1. data, i.e. the data in wide format to be transformed into long format
2. key, the variable name of a new column which will enumerate the different categories possible
3. value, the variable name of a new column which will contain the actual data
4. and a selection of columns which will be converted into the entries in your new column.

data_wide = baker2016[,c(20,21,27)] # This is the data we will be using for our plot in wide format 
data_long = gather(data = data_wide, key = question, value = response, 1:2) # Convert the data into long format

The data in wide format allows us to plot something like this for example:

# Delete responses where participants couldn't remember the result
data_long = data_long[-which((data_long$response == "I can't remember")),]

ggplot(data_long[data_long$question=='try_failElse',], aes(x = field, fill=response)) + 
  geom_bar(aes(y = ..count../tapply(..count.., ..x.. ,sum)[..x..]), position = 'dodge',group = 1) +
  scale_fill_manual(values = c("red", "orange"))+
  coord_flip()

Prettier plots

You can change a whole range of aspects about a theme. A comprehensive list of options can be found here. Here are a few basic things to get started with:

ggplot(data_long[data_long$question=='try_failElse',], aes(x = field, fill=response)) + 
  geom_bar(aes(y = ..count../tapply(..count.., ..x.. ,sum)[..x..]), position = 'dodge',group = 1) +
  scale_fill_manual(values = c("red", "orange"))+
  coord_flip()

ggplot(data_long[data_long$question=='try_failElse',], aes(x = field, fill=response)) + 
  geom_bar(aes(y = ..count../tapply(..count.., ..x.. ,sum)[..x..]), position = 'dodge',group = 1) +
  scale_fill_brewer(palette = 'Set3')+
  coord_flip()

ggplot(baker2016, aes(x= flagged, fill = as.factor(ifelse(flagged=='Not enough',1,2)))) + 
  geom_bar()+
  xlab('Response')+
  ylab('Count')+
  ggtitle("How familiar are you with the term 'Open Science'")

ggplot(baker2016, aes(x= fct_infreq(flagged), fill = as.factor(ifelse(flagged=='Not enough',1,2)))) + 
  geom_bar()

ggplot(baker2016, aes(x=dummy_age, y=dummy_dat2)) + 
  geom_point(shape=8)+
  geom_smooth(method = 'lm')

ggplot(baker2016, aes(x=dummy_age, y=dummy_dat2, alpha = 0.2, color=as.factor(dummy_group))) + 
  geom_point()+
  geom_smooth(method = 'lm')

ggplot(baker2016, aes(x= fct_infreq(flagged), fill = as.factor(ifelse(flagged=='Not enough',1,2)))) + 
  geom_bar()+
  xlab('Response')+
  ylab('Count')+
  ggtitle("How familiar are you with the term 'Open Science'")

ggplot(baker2016, aes(x= fct_infreq(flagged), fill = as.factor(ifelse(flagged=='Not enough',1,2)))) + 
  geom_bar()+
  xlab('Response')+
  ylab('Count')+
  ggtitle("How familiar are you with the term 'Open Science'")+
  theme_bw()

ggplot(baker2016, aes(x= fct_infreq(flagged), fill = as.factor(ifelse(flagged=='Not enough',1,2)))) + 
  geom_bar()+
  xlab('Response')+
  ylab('Count')+
  ggtitle("How familiar are you with the term 'Open Science'")+
  theme_bw() +
  labs(fill='') 

pdf('tmp.pdf',4,5)
ggplot(baker2016, aes(x= fct_infreq(flagged), fill = as.factor(ifelse(flagged=='Not enough',1,2)))) + 
  geom_bar()+
  xlab('Response')+
  ylab('Count')+
  ggtitle("How familiar are you with the term 'Open Science'")+
  theme_bw() +
  theme(legend.position = "none")
dev.off()

## quartz_off_screen 
##                 2

Further Resources

This website could be considered the bible of plotting data in R. This ggplot cheatsheet is great to have at hand.